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Record W3167216532 · doi:10.1177/11786302211014400

Assessment of Decontamination and Reuse of Disposable Filter Funnels Used in Microbiological Water Quality Tests

2021· article· en· W3167216532 on OpenAlexafffund
Camille Zimmer, Alexandra Cassivi, Célia Ceolin Baía, Elizabeth Tilley, Robert Bain, Richard B. Johnston, Caetano C. Dorea

Bibliographic record

VenueEnvironmental Health Insights · 2021
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaUNICEFWorld Health Organization
KeywordsHuman decontaminationEnvironmental scienceReuseWater qualityQuality assessmentWaste managementToxicologyEngineeringEvaluation methodsReliability engineeringBiology

Abstract

fetched live from OpenAlex

into nationally-representative household surveys such as the Multiple Indicator Cluster Surveys (MICS). However, plastic waste generated during such water quality testing programs, mostly through the use of pre-sterilized disposable materials, is non-negligible. The objective of this study was to evaluate several re-use protocols for disposable filter funnels used by the MICS water quality test kits. Decontamination and re-use protocols were assessed in centralized laboratory and decentralized field settings and neither yielded positive results. Re-use of 100 mL sterile funnels decontaminated with an alcohol wipe resulted in a higher incidence of false positive results (i.e., positive contamination when processing sterile water), both in the laboratory and field; therefore, a higher proportion of positives tests can be expected if these components are re-used. Further improvements to the decontamination technique and training are needed before material re-use can be reliably adopted. Autoclaving the funnels for re-use is feasible, provided that there is capacity to re-package and distribute funnels in a sterile manner.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.320

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.032
GPT teacher head0.333
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2021
Admission routes2
Has abstractyes

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